Identifying key media events and modeling causal relationships between key events and reported feelings

ABSTRACT

A novel approach enables an event-based framework for evaluating a media instance based on key events of the media instance. First, physiological responses are derived and aggregated from the physiological data of viewers of the media instance. The key events in the media instance can then be identified, wherein such key events drive and determine the viewers&#39; responses to the media instance. Causal relationship between the viewers&#39; responses to the key events and their surveyed feelings about the media instance can further be established to identify why and what might have caused the viewers to feel the way they do.

BACKGROUND

Advertisers, media producers, educators and other relevant parties havelong desired to understand the responses their targets—customers,clients and pupils—have to their particular stimulus in order to tailortheir information or media instances to better suit the needs of thesetargets and/or to increase the effectiveness of the media instancecreated. A key to making a high performing media instance is to makesure that every event in the media instance elicits the desiredresponses from the viewers, not responses very different from what thecreator of the media instance expected. The media instance herein can bebut is not limited to, a video, an advertisement clip, a movie, acomputer application, a printed media (e.g., a magazine), a video game,a website, an online advertisement, a recorded video, a liveperformance, a debate, and other types of media instance from which aviewer can learn information or be emotionally impacted.

It is well established that physiological response is a validmeasurement for viewers' changes in emotions and an effective mediainstance that connects with its audience/viewers is able to elicit thedesired physiological responses from the viewers. Every media instancemay have its key events/moments—moments which, if they do not evoke theintended physiological responses from the viewers, the effectiveness ofthe media instance may suffer significantly. For a non-limiting example,if an ad is intended to engage the viewers by making them laugh, but theviewers do not find a 2-second-long punch-line funny, such negativeresponses to this small piece of the ad may drive the overall reactionto the ad. Although survey questions such as “do you like this ad ornot” have long been used to gather viewers' subjective reactions to amedia instance, they are unable to provide more insight into why andwhat have caused the viewers reacted in the way they did.

SUMMARY

An approach enables an event-based framework for evaluating a mediainstance based on key events of the media instance. First, physiologicalresponses are derived and aggregated from the physiological data ofviewers of the media instance. The key events in the media instance canthen be identified, wherein such key events drive and determine theviewers' responses to the media instance. Causal relationship betweenthe viewers' responses to the key events and their surveyed feelingsabout the media instance can further be established to identify why andwhat might have caused the viewers to feel the way they do.

Such an approach provides information that can be leveraged by a creatorof the media instance to improve the media instance. For a non-limitingexample, if a joke in an advertisement is found to drive purchase intentof the product advertised, but the advertisement's target demographicdoes not respond to the joke, the joke can be changed so that theadvertisement achieves its goal: increasing product purchase intent inthe target demographic.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter. These andother advantages of the present invention will become apparent to thoseskilled in the art upon a reading of the following descriptions and astudy of the several figures of the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example of a system to support identification of keyevents in a media instance that drive physiological responses fromviewers.

FIG. 2 depicts a flowchart of an exemplary process to supportidentification of key events in a media instance that drivephysiological responses from viewers.

FIGS. 3( a)-(c) depict exemplary traces of physiological responsesmeasured and exemplary dividing lines of events in a media instance.

FIGS. 4( a)-(c) depict exemplary event identification results based ondifferent event defining approaches.

FIG. 5 depicts results from exemplary multivariate regression runs onevents in an advertisement to determine which events drive the viewers'responses the most.

FIGS. 6( a)-(b) depict exemplary correlations between physiologicalresponses from viewers to key jokes in an ad and the surveyed intent ofthe viewers to tell others about the ad.

DETAILED DESCRIPTION

The invention is illustrated by way of example and not by way oflimitation in the figures of the accompanying drawings in which likereferences indicate similar elements. It should be noted that referencesto “an” or “one” or “some” embodiment(s) in this disclosure are notnecessarily to the same embodiment, and such references mean at leastone. Although the subject matter is described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

Although the diagrams depict components as functionally separate, suchdepiction is merely for illustrative purposes. It will be apparent tothose skilled in the art that the components portrayed in this figurecan be arbitrarily combined or divided into separate software, firmwareand/or hardware components. Furthermore, it will also be apparent tothose skilled in the art that such components, regardless of how theyare combined or divided, can execute on the same computing device ormultiple computing devices, and wherein the multiple computing devicescan be connected by one or more networks.

Physiological data, which includes but is not limited to heart rate,brain waves, electroencephalogram (EEG) signals, blink rate, breathing,motion, muscle movement, galvanic skin response and any other responsecorrelated with changes in emotion of a viewer of a media instance, cangive a trace (e.g., a line drawn by a recording instrument) of theviewer's responses while he/she is watching the media instance. Thephysiological data can be measure by one or more physiological sensors,each of which can be but is not limited to, an electroencephalogram, anaccelerometer, a blood oxygen sensor, a galvanometer, an electromygraph,skin temperature sensor, breathing sensor, and any other physiologicalsensor.

The physiological data in the human body of a viewer has been shown tocorrelate with the viewer's change in emotions. Thus, from the measured“low level” physiological data, “high level” (i.e., easier tounderstand, intuitive to look at) physiological responses from theviewers of the media instance can be created. An effective mediainstance that connects with its audience/viewers is able to elicit thedesired emotional response. Here, the high level physiological responsesinclude, but are not limited to, liking (valence)—positive/negativeresponses to events in the media instance, intent to purchase or recall,emotional engagement in the media instance, thinking—amount of thoughtsand/or immersion in the experience of the media instance,adrenaline—anger, distraction, frustration, and other emotionalexperiences to events in the media instance. In addition, thephysiological responses may also include responses to other types ofsensory stimulations, such as taste and/or smell, if the subject matteris food or a scented product instead of a media instance.

FIG. 1 depicts an example of a system 100 to support identification ofkey events in a media instance that drives physiological responses fromviewers. In the example of FIG. 1, the system 100 includes a responsemodule 102, an event defining module 104, a key event module 106, and areaction database 108.

The response module 102 is a software component which while inoperation, first accepts and/or records physiological data from each ofa plurality of viewers watching a media instance, then derives andaggregates physiological responses from the collected physiologicaldata. Such derivation can be accomplished via a plurality of statisticalmeasures, which include but are not limited to, average value, deviationfrom mean, 1st order derivative of the average value, 2nd orderderivative of the average value, coherence, positive response, negativeresponse, etc., using the physiological data of the viewers as inputs.Facial expression recognition, “knob” and other measures of emotion canalso be used as inputs with comparable validity. Here, the physiologicaldata may be either be retrieved from a storage device or measured viaone or more physiological sensors, each of which can be but is notlimited to, an electroencephalogram, an accelerometer, a blood oxygensensor, a galvanometer, an electromygraph, and any other physiologicalsensor either in separate or integrated form. The derived physiologicalresponses can then be aggregated over the plurality of viewers watchingone or more media instances.

The event defining module 104 is a software component which while inoperation, defines and marks occurrences and durations of a plurality ofevents happening in the media instance. The duration of each of event inthe media instance can be constant, non-linear, or semi-linear in time.Such event definition may happen either before or after thephysiological data of the plurality of viewers has been measured, wherein the later case, the media instance can be defined into the pluralityof events based on the physiological data measured from the plurality ofviewers.

The key event module 106 is a software component which while inoperation, identifies one or more key events in the media instance andreports the key events to an interested party of the media instance,wherein the key events drive and determine the viewers' physiologicalresponses to the media instance. Key events in the media instance can beused to pinpoint whether and/or which part of the media instance need tobe improved or changed, and which part of the media instance should bekept intact. For non-limiting examples, the key event module mayidentify which key event(s) in the media instance trigger the mostpositive or negative responses from the viewers, or alternatively, whichkey event(s) are polarizing events, e.g., they cause large discrepanciesin the physiological responses from different demographic groups ofviewers, such as between groups of men and women, when the groups aredefined by demographic characteristics. In addition, the key eventmodule is operable to establish a causal relationship between theviewers' responses to the events in the media instance and theirsurveyed feelings about the media instance so that creator of the mediainstance may gain insight into the reason why and what key events mighthave caused the viewers to feel the way they do.

The reaction database 108 stores pertinent data of the media instancethe viewers are watching, wherein the pertinent data includes but is notlimited to survey questions and results asked for each of the pluralityof viewers before, during, and/or after their viewing of the mediainstance. In addition, the pertinent data may also include but is notlimited to the following:

-   -   Events/moments break down of the media instance;    -   Key events in the media instance;    -   Metadata of the media instance, which can include but is not        limited to, production company, brand, product name, category        (for non-limiting examples, alcoholic beverages, automobiles,        etc), year produced, target demographic (for non-limiting        examples, age, gender, income, etc) of the media instance.    -   If the subject matter is food or a scented product instead of a        media instance, the surveyed reactions to the taste or smell of        a key ingredient in the food or scented product.        Here, the term database is used broadly to include any known or        convenient means for storing data, whether centralized or        distributed, relational or otherwise.

While the system 100 depicted in FIG. 1 is in operation, the responsemodule 102 derives aggregated physiological responses from thephysiological data of a plurality of viewers watching a media instance.The key event module 106 identifies, among the plurality of events inthe media instance as defined by the event defining module 104, one ormore key events that drive and determine the viewers' physiologicalresponses to the media instance based on the aggregated physiologicalresponses from the viewers. In addition, the key event module 106 mayretrieve outcomes to questions surveyed from the viewers of the mediainstance from the reaction database 108, and correlates the viewers'responses to the key events and their surveyed feelings about the mediainstance to determine what might have caused the viewers to feel the waythey do. The entire approach can also be automated as each step of theapproach can be processed by a computing device, allowing for objectivemeasure of a media without much human input or intervention.

FIG. 2 depicts a flowchart of an exemplary process to supportidentification of key events in a media instance that drivephysiological responses from viewers. Although this figure depictsfunctional steps in a particular order for purposes of illustration, theprocess is not limited to any particular order or arrangement of steps.One skilled in the art will appreciate that the various steps portrayedin this figure could be omitted, rearranged, combined and/or adapted invarious ways.

Referring to FIG. 2, physiological responses can be derived andaggregated from the physiological data of a plurality of viewerswatching a media instance at block 202. At block 204, the media instancecan be defined into a plurality of events, and correlation between thephysiological responses from the viewers to the key events in the mediainstance and a surveyed outcome of their feelings about the mediainstance can optionally be established at block 206. At block 208, keyevents in the media instance can be identified based on the aggregatedphysiological responses from the viewers and/or the correlation betweenthe physiological responses and a surveyed outcome. Finally, the keyevents and/or their correlation with the surveyed outcome are reportedto an interested party of the media instance at block 210, wherein theinterested party may then improve the media instance based on the keyevents and/or the correlations.

Events Definition

In some embodiments, the event defining module 104 is operable to defineoccurrence and duration of events in the media instance based on salientpositions identified in the media instance. Once salient positions inthe media instance are identified, the events corresponding to thesalient positions can be extracted. For a non-limiting example, an eventin a video game may be defined as a “battle tank” appearing in theplayer's screen and lasting as long as it remains on the screen. Foranother non-limiting example, an event in a movie may be defined asoccurring every time a joke is made. While defining humor is difficult,punch line events that are unexpected, absurd, and comically exaggeratedoften qualify as joke events.

FIG. 3( a) shows an exemplary trace of the physiologicalresponse—“Engagement” for a player playing Call of Duty 3 on the Xbox360. The trace is a time series, with the beginning of the session onthe left and the end on the right. Two event instances 301 and 302 arecircled, where 301 on the left shows low “Engagement” during a game playthat happens during a boring tutorial section. 302 shows a high“Engagement” section that has been recorded when the player experiencesthe first battle of the game. FIG. 3( b) shows exemplary vertical linesthat divide a piece of media instance into many events defining everyimportant thing that a player of the video game or other media mayencounter and/or interact with.

In some embodiments, the event defining module 104 is operable to defineoccurrence and duration of events in the media instance via at least theone or more of the following approaches. The events so identified by theevent defining module 104 are then provided to the key event module 106to test for “significance” as key events in the media instance asdescribed below.

-   -   The hypothesis approach, which utilizes human hypothesis to        identify events in the media instance, wherein such events shall        be tested for significance as key events.    -   The small pieces or time shift approach, which breaks the media        instance into small pieces in time, and scans each small piece        for significant switch in the viewers' responses, wherein        consecutive significant small pieces can be integrated as one        key event. For the non-limiting example of FIG. 4( a), the small        pieces are each ⅕ second in length and consecutive small pieces        that are found to be significant indicate an event, such as 401        and 402. For the exemplary car ad shown in FIG. 4( b), 403        represents the first 10 seconds of the car ad as a        cross-component event, and 404 represents a cross-ad music        event.    -   The turning point approach, which finds where the aggregated        physiological responses (traces), first derivative, and second        derivative of aggregated trace(s) have roots and uses them as        possible event cut points (delimiters). Here, roots of the        aggregate traces can be interpreted as points when the viewers'        aggregated physiological responses transition from above average        to below average, or from positive to negative. Roots in the        first derivative of the aggregate traces can be interpreted as        ‘turning points’, at which the physiological responses        transition from a state of increasing positivity to increasing        negativity, or vice versa. Roots in the second derivative of the        aggregate traces can also be interpreted as ‘turning points’,        points, at which the physiological responses begin to slow down        the rate of increase in positivity. All such roots are then        collected in a set s. For every pair i,j of roots in the set s        for which j occurs after i in the media instance, the event        which starts at i and ends at j is tested for significance as a        key event. Note here that i and j do not have to be consecutive        in time.    -   The multi-component event approach, which breaks the media        instance down into components and then divides each component        into events. A media instance typically has many components. For        a non-limiting example, an advertisement can have one or more        of: voiceover, music, branding, and visual components. All        points in the media instance for which there is a significant        change in one of the components, such as when the voiceover        starts and ends, can be human marked. As with the turning point        approach, all the marked points can be collected in the set s.        For every pair i,j of roots in the set s for which j occurs        after i in the media instance, the event which starts at i and        ends at j is tested for significance as a key event. While this        approach requires greater initial human input, it may provide        more precise, more robust results based on automated        higher-level analysis and the benefits would outweigh the costs.        For a non-limiting example, a car ad can be broken down into        visual, dialogue, music, text, and branding components, each        with one or more events. For the exemplary car ad shown in FIG.        4( c), 405 represents a visual event, 406 represents a dialogue        event, and 407 represents a music event.

Key Events Identification

In some embodiments, the key event module 106 is operable to accept theevents defined by the event defining module 104 and automatically spotstatistically significant/important points in the aggregatedphysiological responses from the viewers relevant to identify the keymoments/events in the media instance. More specifically, the key eventmodule is operable to determine one or more of:

-   -   if an event polarizes the viewers, i.e., the physiological        responses from the viewers are either strongly positive or        strongly negative.    -   if the physiological responses vary significantly by a        demographic factor.    -   if the physiological responses are significantly correlated with        the survey results.    -   if an event ranks outstandingly high or low compared to similar        events in other media instances        For a non-limiting example, FIG. 3( c) shows two exemplary        traces of the “Engagement” response of a video game player where        the boxes 303, 304, and 305 in the pictures correspond to        “weapon use” events. At each point where the events appear,        “Engagement” rises sharply, indicating that the events are key        events for the video game.

In some embodiments, the key events found can be used to improve themedia instance. Here “improving the media instance” can be defined as,but is not limited to, changing the media instance so that it is morelikely to achieve the goals of the interested party or creator of themedia instance.

In some embodiments, the key event module 106 is further operable toestablish a casual relationship between surveyed feelings about themedia instance and the key events identified based on the physiologicalresponses from the viewers. In other words, it establishes a correlationbetween the physiological responses from the viewers to key events inthe media instance and a surveyed outcome, i.e., the viewers' reportedfeelings on a survey, and reports to the interested parties (e.g.creator of the event) which key events in the media instance actuallycaused the outcome. Here, the outcome can include but is not limited to,liking, effectiveness, purchase intent, post viewing product selection,etc. For a non-limiting example, if the viewers indicate on a surveythat they did not like the media instance, something about the mediainstance might have caused them to feel this way. While the cause may bea reaction to the media instance in general, it can often be pinned downto a reaction to one or more key events in the media instance asdiscussed above. The established casual relationship explains why theviewers report on the survey their general feelings about the mediainstance the way they do without human input.

In some embodiments, the key event module 106 is operable to adoptmultivariate regression analysis via a multivariate model thatincorporates the physiological responses from the viewers as well as thesurveyed feelings from the viewers to determine which events, onaverage, are key events in driving reported feelings (surveyed outcome)about the media instance. Here, the multivariate regression analysisexamines the relationship among many factors (the independent variables)and a single, dependent variable, which variation is thought to be atleast partially explained by the independent variables. For anon-limiting example, the amount of rain that falls on a given dayvaries, so there is variation in daily rainfall. Both the humidity inthe air and the number of clouds in the sky on a given day can behypothesized to explain this variation in daily rainfall. Thishypothesis can be tested via multivariate regression, with dailyrainfall as the dependent variable, and humidity and number of clouds asindependent variables.

In some embodiments, the multivariate model may have each individualviewer's reactions to certain key events in the media instance asindependent variables and their reported feeling about the mediainstance as the dependent variable. The coefficients from regressing theindependent variables on the dependent variable would determine whichkey events are causing the reported feelings. Such a multivariate modelcould be adopted here to determine what set of key events most stronglyaffect reported feelings from the viewers about the media instance, suchas a joke in an advertisement. One characterization of such event(s) isthat the more positive (negative) the viewers respond to the event(s),the more likely the viewers were to express positive feelings about themedia instance. For a non-limiting example, a multivariate regressioncan be run on multiples events (1, 2 . . . n) within an entire montagesequence of an advertisement to determine which events drive liking themost, using relationship between reported feelings about the ad and theemotional responses from the viewers to the events in the ad as input.The results of the multivariate regression runs shown in FIG. 5 indicatethat 2 out of the 6 events tested in the ad drive the viewers' responsesthe most, while the other 4 events do not meet the threshold forexplanatory power.

In an automated process, this multivariate regression may be runstepwise, which essentially tries various combinations of independentvariables, determining which combination has the strongest explanatorypower. This is a step toward creating the causal relationship betweenthe viewers' responses to the events and their surveyed feelings aboutthe media instance. For a non-limiting example, if response to joke #2is correlated with indicated intent to purchase when holding genders andresponses to jokes #1 and #3 constant, a causal conclusion can be madethat joke #2 triggers the viewers' intent to purchase.

In some embodiments, the key event module 106 identifies the keypolarizing event(s) that cause statistically significant difference inthe surveyed outcome from different demographic groups of viewers andprovides insight into, for non-limiting examples, why women do not likethe show or which issue actually divides people in a political debate.The key event module 106 may collect demographic data from overallpopulation of the viewers and categorize them into groups todifferentiate the responses for the subset, wherein the viewers can begrouped one or more of: race, gender, age, education, demographics,income, buying habits, intent to purchase, and intent to tell. Suchgrouping information can be included in the regressions to determine howdifferent groups report different reactions to the media instance in thesurvey. Furthermore, grouping/event response interaction variables canbe included to determine how different groups respond differently to thekey events in the media instance. For key events that are polarizing,demographic information and/or interaction variables of the viewers canalso be included to the multivariate model capture the combined effectof the demographic factor and the reaction to the polarizing key events.

For a non-limiting example, the viewers of an ad can be first asked asurvey question, “How likely are you to tell someone about thisparticular commercial—meaning tell a friend about this ad you've justseen” as shown in FIG. 6( a). The viewers of the ad are broken into twogroups based on indicated likelihood to tell someone about thecommercial—the affirmative group and the negative group, and it isassumed that the viewers in both groups are normally distributed withthe same variance. The emotional responses from the viewers in thegroups to two key jokes 601 and 602 in the ad are then compared to theirsurveyed reactions to test the following hypothesis—“the group thatindicated they were likely to tell someone will have a stronger positivephysiological response to the two key jokes than the group thatindicated they were unlikely to tell someone.” The affirmative groupthat indicated they were likely to tell a friend had, on average, a morepositive reaction to both jokes than the negative group that indicatedthey were unlikely to tell a friend. In both cases, experiments usingthe change in individual liking as the metric to measure thephysiological response rejects the null hypothesis—that there was nodifference in emotional response to the jokes between the two groups—atabove the 95% confidence level. Referring to graphs in FIG. 6( a), the Xaxis displays the testers' likelihood to tell a friend as indicated onthe post-exposure survey and the Y axis displays the testers' emotionalresponse to the joke. The triangles represent the “Go” people—those whoindicated they were likely (to varying degrees) to tell their friendabout the spot. The circles represent those who indicated that they wereunlikely to do so. Note the upward trends—the more positive theemotional reaction to the joke, the greater indicated likelihood to tella friend about the spot. FIG. 6( b) further summarizes the physiologicalresponses to both jokes, where the reaction to the Crying Wife is on theX axis and the reaction to the Dead Husband is on the Y axis. Animaginary line around is drawn around the viewers who reacted relativelypositively to both jokes. Note that this line corrals most of the blackdiamonds, which represent viewers who indicated they would tell a friendabout the ad.

One embodiment may be implemented using a conventional general purposeor a specialized digital computer or microprocessor(s) programmedaccording to the teachings of the present disclosure, as will beapparent to those skilled in the computer art. Appropriate softwarecoding can readily be prepared by skilled programmers based on theteachings of the present disclosure, as will be apparent to thoseskilled in the software art. The invention may also be implemented bythe preparation of integrated circuits or by interconnecting anappropriate network of conventional component circuits, as will bereadily apparent to those skilled in the art.

One embodiment includes a computer program product which is a machinereadable medium (media) having instructions stored thereon/in which canbe used to program one or more computing devices to perform any of thefeatures presented herein. The machine readable medium can include, butis not limited to, one or more types of disks including floppy disks,optical discs, DVD, CD-ROMs, micro drive, and magneto-optical disks,ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices,magnetic or optical cards, nanosystems (including molecular memory ICs),or any type of media or device suitable for storing instructions and/ordata. Stored on any one of the computer readable medium (media), thepresent invention includes software for controlling both the hardware ofthe general purpose/specialized computer or microprocessor, and forenabling the computer or microprocessor to interact with a human vieweror other mechanism utilizing the results of the present invention. Suchsoftware may include, but is not limited to, device drivers, operatingsystems, execution environments/containers, and applications.

The foregoing description of the preferred embodiments of the presentinvention has been provided for the purposes of illustration anddescription. It is not intended to be exhaustive or to limit theinvention to the precise forms disclosed. Many modifications andvariations will be apparent to the practitioner skilled in the art.Particularly, while the concept “module” is used in the embodiments ofthe systems and methods described above, it will be evident that suchconcept can be interchangeably used with equivalent concepts such as,class, method, type, interface, bean, component, object model, and othersuitable concepts. Embodiments were chosen and described in order tobest describe the principles of the invention and its practicalapplication, thereby enabling others skilled in the art to understandthe invention, the various embodiments and with various modificationsthat are suited to the particular use contemplated. It is intended thatthe scope of the invention be defined by the following claims and theirequivalents.

1. A system to support key events identification, comprising: a responsemodule embedded in a first readable medium which, while in operation:accepts and/or records physiological data from each of a plurality ofviewers watching a media instance; derives and aggregates one or morephysiological responses from the collected physiological data; an eventdefining module embedded in a second readable medium which, while inoperation, defines the media instance into a plurality of events; a keyevent module embedded in a third readable medium which, while inoperation: accepts the plurality of events defined in the mediainstance; identifies one or more key events in the media instance,wherein the one or more key events drive the one or more physiologicalresponses to the media instance from the plurality of viewers; reportsthe one or more key events to an interested party of the media instance.2. The system of claim 1, further comprising: a reaction database whichstores pertinent data of the media instance, wherein such pertinent dataincludes survey questions and results asked for each of the plurality ofviewers before, during, and/or after their viewing of the mediainstance.
 3. The system of claim 1, wherein: the media instance is oneof: a video, an advertisement clip, a movie, a computer application, aprinted media, a video game, a website, an online advertisement, arecorded video, a live performance, a debate, and other types of mediainstance from which a viewer can learn information or be emotionallyimpacted.
 4. The system of claim 1, wherein: the physiological data isone or more of: heart rate, brain waves, electroencephalogram (EEG)signals, blink rate, breathing, motion, muscle movement, galvanic skinresponse and any other response correlated with changes in emotion of aviewer of a media instance.
 5. The system of claim 1, wherein: each ofthe one or more physiological responses is one of: liking, intent topurchase or recall, emotional engagement, thinking, adrenaline, otheremotional experiences to the media instance, and other response tosensory stimulations from food or a scented product.
 6. The system ofclaim 1, wherein: the response module either retrieves the physiologicaldata from a storage device or measures the data via one or morephysiological sensors attached to each of the plurality of viewerseither in separate or integrated form.
 7. The system of claim 1,wherein: the response module derives the one or more physiologicalresponses via a plurality of statistical measures.
 8. The system ofclaim 1, wherein: the event defining module defines occurrence andduration of the plurality of events in the media instance by identifyingsalient positions in the media instance.
 9. The system of claim 1,wherein: the event defining module defines occurrence and duration ofthe plurality of events in the media instance via one or more of: ahypothesis approach, a time shift approach, a turning point approach,and a multi-component event approach.
 10. The system of claim 1,wherein: the key event module automatically spots statisticallysignificant points in the one or more physiological responses that arerelevant to identify the one or more key events in the media instance.11. The system of claim 1, wherein: the key event module, while inoperation, collects demographic data of the plurality of viewers;categorizes the plurality of viewers into a plurality of groups based onone or more of: race, gender, age, education, demographics, income,buying habits, intent to purchase, and intent to tell; identifies thekey events that are polarizing and cause statistically significantdifference in the physiological responses from the plurality of groupsof the plurality of viewers.
 12. A system to support key eventsidentification, comprising: a response module embedded in a firstreadable medium which, while in operation: accepts and/or recordsphysiological data from each of a plurality of viewers watching a mediainstance; derives and aggregates one or more physiological responsesfrom the collected physiological data; an event defining module embeddedin a second readable medium which, while in operation, defines the mediainstance into a plurality of events; a key event module embedded in athird readable medium which, while in operation: retrieves from thereaction database a surveyed outcome from the plurality of viewers ofthe media instance; establishes a causal relationship between thephysiological responses from the plurality of viewers to the pluralityof events and the surveyed outcome of their feelings about the mediainstance; identifies one or more key events in the media instance basedon the correlation between the one or more physiological responses andthe surveyed outcome.
 13. The system of claim 12, wherein: the surveyedoutcome is one of: liking, effectiveness, purchase intent, and postviewing product selection.
 14. The system of claim 12, wherein: the keyevent module runs multivariate regression analysis via a multivariatemodel that incorporates the physiological responses from the viewers aswell as the survey outcome from the viewers to identify which of the oneor more key events drive the surveyed outcome.
 15. The system of claim12, wherein: the key event module reports to the interested party whichof the one or more key events in the media instance actually cause thesurveyed outcome.
 16. A method to support key events identification,comprising: deriving and aggregating one or more physiological responsesbased on physiological data from a plurality of viewers watching a mediainstance; defining the media instance into a plurality of events;identifying one or more key events in the media instance based on theone or more physiological responses from the plurality of viewers;reporting the one or more key events to an interested party of the mediainstance.
 17. The method of claim 16, further comprising: retrieving thephysiological data from a storage device or measuring the data via oneor more physiological sensors attached to each of the plurality ofviewers either in separate or integrated form.
 18. The method of claim16, further comprising: deriving the one or more physiological responsesvia a plurality of statistical measures.
 19. The method of claim 16,further comprising: spotting statistically significant points in the oneor more physiological responses that are relevant to identify the one ormore key events in the media instance automatically.
 20. The method ofclaim 16, further comprising: collecting demographic data of theplurality of viewers; categorizing the plurality of viewers into aplurality of groups based on one or more of: race, gender, age,education, demographics, income, buying habits, intent to purchase, andintent to tell; identifying the key events that are polarizing and causestatistically significant difference in the physiological responses fromthe plurality of groups of the plurality of viewers.
 21. The method ofclaim 15, further comprising: improving the media instance based on thephysiological responses from the plurality of viewers to the one or morekey events.
 22. The method to support key events identification, furthercomprising: deriving and aggregating one or more physiological responsesbased on physiological data from a plurality of viewers watching a mediainstance; defining the media instance into a plurality of events;retrieving a surveyed outcome from the plurality of viewers of the mediainstance; establishing a correlation between the one or morephysiological responses to the plurality of events in the media instanceand the surveyed outcome of feelings of the plurality of viewers aboutthe media instance; identifying one or more key events in the mediainstance based on the correlation between the one or more physiologicalresponses and the surveyed outcome.
 23. The method of claim 22, furthercomprising: running multivariate regression analysis via a multivariatemodel that incorporates the physiological responses from the viewers aswell as the survey outcome from the viewers to identify which of the oneor more key events drive the surveyed outcome.
 24. The method of claim22, further comprising: reporting to the interested party which of theone or more key events in the media instance actually cause the surveyedoutcome.
 25. A machine readable medium having instructions storedthereon that when executed cause a system to: derive and aggregate oneor more physiological responses based on physiological data from aplurality of viewers watching a media instance; define the mediainstance into a plurality of events; identify one or more key events inthe media instance based on the one or more physiological responses fromthe plurality of viewers or a correlation between the one or morephysiological responses and a surveyed outcome from the plurality ofviewers; report the one or more key events to an interested party of themedia instance.